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cs.AR2020
HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks
James Garland, David Gregg
Convolutional neural networks (CNNs) are typically trained using 16- or 32-bit floating-point (FP) and researchers show that low-precision floating-point (FP) can be highly effecti…
cs.AR2018
Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing
James Garland, David Gregg
Convolutional neural networks (CNNs) are one of the most successful machine learning techniques for image, voice and video processing. CNNs require large amounts of processing capa…